Overview

Dataset statistics

Number of variables23
Number of observations196694
Missing cells0
Missing cells (%)0.0%
Duplicate rows119
Duplicate rows (%)0.1%
Total size in memory36.0 MiB
Average record size in memory192.0 B

Variable types

Categorical11
DateTime2
Text6
Numeric4

Alerts

Year has constant value "2022-23"Constant
Billing grp has constant value "LFS"Constant
Channel-1 has constant value "LFS"Constant
Dataset has 119 (0.1%) duplicate rowsDuplicates
Brand is highly imbalanced (96.2%)Imbalance
Channel is highly imbalanced (50.9%)Imbalance
Quantity is highly skewed (γ1 = 36.44210692)Skewed

Reproduction

Analysis started2024-05-27 06:16:19.899104
Analysis finished2024-05-27 06:16:32.258979
Duration12.36 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2022-23
196694 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1376858
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-23
2nd row2022-23
3rd row2022-23
4th row2022-23
5th row2022-23

Common Values

ValueCountFrequency (%)
2022-23 196694
100.0%

Length

2024-05-27T11:46:32.342677image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T11:46:32.465928image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2022-23 196694
100.0%

Most occurring characters

ValueCountFrequency (%)
2 786776
57.1%
0 196694
 
14.3%
- 196694
 
14.3%
3 196694
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1180164
85.7%
Dash Punctuation 196694
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 786776
66.7%
0 196694
 
16.7%
3 196694
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 196694
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1376858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 786776
57.1%
0 196694
 
14.3%
- 196694
 
14.3%
3 196694
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1376858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 786776
57.1%
0 196694
 
14.3%
- 196694
 
14.3%
3 196694
 
14.3%

Month
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2022-04-01 00:00:00
Maximum2023-03-01 00:00:00
2024-05-27T11:46:32.575851image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:32.718649image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

Month Key
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Dec
21326 
Oct
20032 
Jan
17202 
Aug
16414 
Apr
16315 
Other values (7)
105405 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters590082
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApr
2nd rowApr
3rd rowApr
4th rowApr
5th rowApr

Common Values

ValueCountFrequency (%)
Dec 21326
10.8%
Oct 20032
10.2%
Jan 17202
8.7%
Aug 16414
8.3%
Apr 16315
8.3%
May 16039
8.2%
Jul 15940
8.1%
Nov 15129
7.7%
Jun 14894
7.6%
Feb 14824
7.5%
Other values (2) 28579
14.5%

Length

2024-05-27T11:46:32.859633image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec 21326
10.8%
oct 20032
10.2%
jan 17202
8.7%
aug 16414
8.3%
apr 16315
8.3%
may 16039
8.2%
jul 15940
8.1%
nov 15129
7.7%
jun 14894
7.6%
feb 14824
7.5%
Other values (2) 28579
14.5%

Most occurring characters

ValueCountFrequency (%)
e 50933
 
8.6%
J 48036
 
8.1%
u 47248
 
8.0%
a 47037
 
8.0%
c 41358
 
7.0%
A 32729
 
5.5%
n 32096
 
5.4%
p 31098
 
5.3%
r 30111
 
5.1%
M 29835
 
5.1%
Other values (12) 199601
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 393388
66.7%
Uppercase Letter 196694
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 50933
12.9%
u 47248
12.0%
a 47037
12.0%
c 41358
10.5%
n 32096
8.2%
p 31098
7.9%
r 30111
7.7%
t 20032
 
5.1%
g 16414
 
4.2%
y 16039
 
4.1%
Other values (4) 61022
15.5%
Uppercase Letter
ValueCountFrequency (%)
J 48036
24.4%
A 32729
16.6%
M 29835
15.2%
D 21326
10.8%
O 20032
10.2%
N 15129
 
7.7%
F 14824
 
7.5%
S 14783
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 590082
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 50933
 
8.6%
J 48036
 
8.1%
u 47248
 
8.0%
a 47037
 
8.0%
c 41358
 
7.0%
A 32729
 
5.5%
n 32096
 
5.4%
p 31098
 
5.3%
r 30111
 
5.1%
M 29835
 
5.1%
Other values (12) 199601
33.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 590082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 50933
 
8.6%
J 48036
 
8.1%
u 47248
 
8.0%
a 47037
 
8.0%
c 41358
 
7.0%
A 32729
 
5.5%
n 32096
 
5.4%
p 31098
 
5.3%
r 30111
 
5.1%
M 29835
 
5.1%
Other values (12) 199601
33.8%

QTR
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Q3
56487 
Q1
47248 
Q2
47137 
Q4
45822 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters393388
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ1
2nd rowQ1
3rd rowQ1
4th rowQ1
5th rowQ1

Common Values

ValueCountFrequency (%)
Q3 56487
28.7%
Q1 47248
24.0%
Q2 47137
24.0%
Q4 45822
23.3%

Length

2024-05-27T11:46:33.017219image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T11:46:33.159502image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
q3 56487
28.7%
q1 47248
24.0%
q2 47137
24.0%
q4 45822
23.3%

Most occurring characters

ValueCountFrequency (%)
Q 196694
50.0%
3 56487
 
14.4%
1 47248
 
12.0%
2 47137
 
12.0%
4 45822
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 196694
50.0%
Decimal Number 196694
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 56487
28.7%
1 47248
24.0%
2 47137
24.0%
4 45822
23.3%
Uppercase Letter
ValueCountFrequency (%)
Q 196694
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196694
50.0%
Common 196694
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 56487
28.7%
1 47248
24.0%
2 47137
24.0%
4 45822
23.3%
Latin
ValueCountFrequency (%)
Q 196694
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 393388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Q 196694
50.0%
3 56487
 
14.4%
1 47248
 
12.0%
2 47137
 
12.0%
4 45822
 
11.6%

Region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
West
59189 
South
51280 
North
49834 
East
36391 

Length

Max length5
Median length5
Mean length4.5140675
Min length4

Characters and Unicode

Total characters887890
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowSouth
4th rowWest
5th rowSouth

Common Values

ValueCountFrequency (%)
West 59189
30.1%
South 51280
26.1%
North 49834
25.3%
East 36391
18.5%

Length

2024-05-27T11:46:33.348862image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T11:46:33.522605image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
west 59189
30.1%
south 51280
26.1%
north 49834
25.3%
east 36391
18.5%

Most occurring characters

ValueCountFrequency (%)
t 196694
22.2%
o 101114
11.4%
h 101114
11.4%
s 95580
10.8%
W 59189
 
6.7%
e 59189
 
6.7%
S 51280
 
5.8%
u 51280
 
5.8%
N 49834
 
5.6%
r 49834
 
5.6%
Other values (2) 72782
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 691196
77.8%
Uppercase Letter 196694
 
22.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 196694
28.5%
o 101114
14.6%
h 101114
14.6%
s 95580
13.8%
e 59189
 
8.6%
u 51280
 
7.4%
r 49834
 
7.2%
a 36391
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
W 59189
30.1%
S 51280
26.1%
N 49834
25.3%
E 36391
18.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 887890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 196694
22.2%
o 101114
11.4%
h 101114
11.4%
s 95580
10.8%
W 59189
 
6.7%
e 59189
 
6.7%
S 51280
 
5.8%
u 51280
 
5.8%
N 49834
 
5.6%
r 49834
 
5.6%
Other values (2) 72782
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 887890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 196694
22.2%
o 101114
11.4%
h 101114
11.4%
s 95580
10.8%
W 59189
 
6.7%
e 59189
 
6.7%
S 51280
 
5.8%
u 51280
 
5.8%
N 49834
 
5.6%
r 49834
 
5.6%
Other values (2) 72782
 
8.2%
Distinct365
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2022-04-01 00:00:00
Maximum2023-03-31 00:00:00
2024-05-27T11:46:33.768732image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:33.988836image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct173
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2024-05-27T11:46:34.487938image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.6216102
Min length7

Characters and Unicode

Total characters1695819
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowNFM03DQ1
2nd rowNDFM13PD1
3rd rowNFM05DQ1
4th rowFW19PC1
5th rowNEFW05PGC
ValueCountFrequency (%)
nfm01dq1 9526
 
4.8%
nffm01pgc 8053
 
4.1%
nffm01cl2 7357
 
3.7%
nffw01cl2 6961
 
3.5%
nfw01dq1 6572
 
3.3%
nefw11pd1 6542
 
3.3%
nfm05dq1 6485
 
3.3%
nffm14ph1 5938
 
3.0%
nffw02pfc 5850
 
3.0%
nfm03dq1 5800
 
2.9%
Other values (163) 127610
64.9%
2024-05-27T11:46:35.164315image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 303252
17.9%
1 234866
13.8%
N 180238
10.6%
P 142292
8.4%
0 138853
8.2%
M 104870
 
6.2%
W 82788
 
4.9%
D 81389
 
4.8%
C 75575
 
4.5%
2 69708
 
4.1%
Other values (15) 281988
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1156666
68.2%
Decimal Number 539153
31.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 303252
26.2%
N 180238
15.6%
P 142292
12.3%
M 104870
 
9.1%
W 82788
 
7.2%
D 81389
 
7.0%
C 75575
 
6.5%
Q 48680
 
4.2%
G 44476
 
3.8%
E 42386
 
3.7%
Other values (5) 50720
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 234866
43.6%
0 138853
25.8%
2 69708
 
12.9%
4 35642
 
6.6%
3 23924
 
4.4%
5 18243
 
3.4%
9 5761
 
1.1%
8 5098
 
0.9%
6 4442
 
0.8%
7 2616
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1156666
68.2%
Common 539153
31.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 303252
26.2%
N 180238
15.6%
P 142292
12.3%
M 104870
 
9.1%
W 82788
 
7.2%
D 81389
 
7.0%
C 75575
 
6.5%
Q 48680
 
4.2%
G 44476
 
3.8%
E 42386
 
3.7%
Other values (5) 50720
 
4.4%
Common
ValueCountFrequency (%)
1 234866
43.6%
0 138853
25.8%
2 69708
 
12.9%
4 35642
 
6.6%
3 23924
 
4.4%
5 18243
 
3.4%
9 5761
 
1.1%
8 5098
 
0.9%
6 4442
 
0.8%
7 2616
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1695819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 303252
17.9%
1 234866
13.8%
N 180238
10.6%
P 142292
8.4%
0 138853
8.2%
M 104870
 
6.2%
W 82788
 
4.9%
D 81389
 
4.8%
C 75575
 
4.5%
2 69708
 
4.1%
Other values (15) 281988
16.6%

Quantity
Real number (ℝ)

SKEWED 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1904125
Minimum-18
Maximum142
Zeros8
Zeros (%)< 0.1%
Negative593
Negative (%)0.3%
Memory size3.0 MiB
2024-05-27T11:46:35.351161image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum142
Range160
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84269779
Coefficient of variation (CV)0.707904
Kurtosis4503.3313
Mean1.1904125
Median Absolute Deviation (MAD)0
Skewness36.442107
Sum234147
Variance0.71013956
MonotonicityNot monotonic
2024-05-27T11:46:35.510170image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 170447
86.7%
2 19461
 
9.9%
3 3804
 
1.9%
4 1169
 
0.6%
-1 570
 
0.3%
5 456
 
0.2%
6 256
 
0.1%
7 153
 
0.1%
8 88
 
< 0.1%
10 51
 
< 0.1%
Other values (30) 239
 
0.1%
ValueCountFrequency (%)
-18 1
 
< 0.1%
-3 3
 
< 0.1%
-2 19
 
< 0.1%
-1 570
 
0.3%
0 8
 
< 0.1%
1 170447
86.7%
2 19461
 
9.9%
3 3804
 
1.9%
4 1169
 
0.6%
5 456
 
0.2%
ValueCountFrequency (%)
142 1
 
< 0.1%
75 1
 
< 0.1%
45 1
 
< 0.1%
42 2
< 0.1%
37 1
 
< 0.1%
33 1
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 3
< 0.1%

Gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
G
104832 
L
82754 
P
 
8813
U
 
295

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters196694
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowL
5th rowL

Common Values

ValueCountFrequency (%)
G 104832
53.3%
L 82754
42.1%
P 8813
 
4.5%
U 295
 
0.1%

Length

2024-05-27T11:46:35.667722image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T11:46:35.861175image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
g 104832
53.3%
l 82754
42.1%
p 8813
 
4.5%
u 295
 
0.1%

Most occurring characters

ValueCountFrequency (%)
G 104832
53.3%
L 82754
42.1%
P 8813
 
4.5%
U 295
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 196694
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 104832
53.3%
L 82754
42.1%
P 8813
 
4.5%
U 295
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 196694
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 104832
53.3%
L 82754
42.1%
P 8813
 
4.5%
U 295
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 104832
53.3%
L 82754
42.1%
P 8813
 
4.5%
U 295
 
0.1%

Brand
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
TF
195887 
FP
 
807

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters393388
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTF
2nd rowTF
3rd rowTF
4th rowTF
5th rowTF

Common Values

ValueCountFrequency (%)
TF 195887
99.6%
FP 807
 
0.4%

Length

2024-05-27T11:46:36.070810image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T11:46:36.208767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
tf 195887
99.6%
fp 807
 
0.4%

Most occurring characters

ValueCountFrequency (%)
F 196694
50.0%
T 195887
49.8%
P 807
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 393388
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 196694
50.0%
T 195887
49.8%
P 807
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 393388
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 196694
50.0%
T 195887
49.8%
P 807
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 393388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 196694
50.0%
T 195887
49.8%
P 807
 
0.2%

Channel
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
SS
101176 
LS
82052 
PT
 
8173
CT
 
3992
RRL
 
1007
Other values (2)
 
294

Length

Max length3
Median length2
Mean length2.0051196
Min length2

Characters and Unicode

Total characters394395
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCN
2nd rowCN
3rd rowCN
4th rowCN
5th rowCN

Common Values

ValueCountFrequency (%)
SS 101176
51.4%
LS 82052
41.7%
PT 8173
 
4.2%
CT 3992
 
2.0%
RRL 1007
 
0.5%
CN 263
 
0.1%
ss 31
 
< 0.1%

Length

2024-05-27T11:46:36.333280image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T11:46:36.474333image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
ss 101207
51.5%
ls 82052
41.7%
pt 8173
 
4.2%
ct 3992
 
2.0%
rrl 1007
 
0.5%
cn 263
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 284404
72.1%
L 83059
 
21.1%
T 12165
 
3.1%
P 8173
 
2.1%
C 4255
 
1.1%
R 2014
 
0.5%
N 263
 
0.1%
s 62
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 394333
> 99.9%
Lowercase Letter 62
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 284404
72.1%
L 83059
 
21.1%
T 12165
 
3.1%
P 8173
 
2.1%
C 4255
 
1.1%
R 2014
 
0.5%
N 263
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
s 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 394395
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 284404
72.1%
L 83059
 
21.1%
T 12165
 
3.1%
P 8173
 
2.1%
C 4255
 
1.1%
R 2014
 
0.5%
N 263
 
0.1%
s 62
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 284404
72.1%
L 83059
 
21.1%
T 12165
 
3.1%
P 8173
 
2.1%
C 4255
 
1.1%
R 2014
 
0.5%
N 263
 
0.1%
s 62
 
< 0.1%
Distinct311
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2024-05-27T11:46:36.958081image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.0688481
Min length5

Characters and Unicode

Total characters997012
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCN1200
2nd rowCN1201
3rd rowCN1201
4th rowCN1206
5th rowCN1212
ValueCountFrequency (%)
ss222 4592
 
2.3%
ls751 3802
 
1.9%
ss223 3746
 
1.9%
ls760 3497
 
1.8%
ss283 2636
 
1.3%
ls758 2541
 
1.3%
ss255 2540
 
1.3%
ss209 2451
 
1.2%
ls741 2232
 
1.1%
ss219 2227
 
1.1%
Other values (301) 166430
84.6%
2024-05-27T11:46:37.690097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 284467
28.5%
2 132830
13.3%
7 108833
 
10.9%
L 83058
 
8.3%
3 72067
 
7.2%
1 65368
 
6.6%
0 54012
 
5.4%
8 36263
 
3.6%
5 35985
 
3.6%
4 34098
 
3.4%
Other values (8) 90031
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 602613
60.4%
Uppercase Letter 394399
39.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 132830
22.0%
7 108833
18.1%
3 72067
12.0%
1 65368
10.8%
0 54012
9.0%
8 36263
 
6.0%
5 35985
 
6.0%
4 34098
 
5.7%
9 31814
 
5.3%
6 31343
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
S 284467
72.1%
L 83058
 
21.1%
T 12167
 
3.1%
P 8173
 
2.1%
C 4255
 
1.1%
R 2014
 
0.5%
N 263
 
0.1%
O 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 602613
60.4%
Latin 394399
39.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 132830
22.0%
7 108833
18.1%
3 72067
12.0%
1 65368
10.8%
0 54012
9.0%
8 36263
 
6.0%
5 35985
 
6.0%
4 34098
 
5.7%
9 31814
 
5.3%
6 31343
 
5.2%
Latin
ValueCountFrequency (%)
S 284467
72.1%
L 83058
 
21.1%
T 12167
 
3.1%
P 8173
 
2.1%
C 4255
 
1.1%
R 2014
 
0.5%
N 263
 
0.1%
O 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 997012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 284467
28.5%
2 132830
13.3%
7 108833
 
10.9%
L 83058
 
8.3%
3 72067
 
7.2%
1 65368
 
6.6%
0 54012
 
5.4%
8 36263
 
3.6%
5 35985
 
3.6%
4 34098
 
3.4%
Other values (8) 90031
 
9.0%
Distinct311
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2024-05-27T11:46:38.208414image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.0688481
Min length5

Characters and Unicode

Total characters997012
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCN1200
2nd rowCN1201
3rd rowCN1201
4th rowCN1206
5th rowCN1212
ValueCountFrequency (%)
ss222 4592
 
2.3%
ls751 3802
 
1.9%
ss223 3746
 
1.9%
ls760 3497
 
1.8%
ss283 2636
 
1.3%
ls758 2541
 
1.3%
ss255 2540
 
1.3%
ss209 2451
 
1.2%
ls741 2232
 
1.1%
ss219 2227
 
1.1%
Other values (301) 166430
84.6%
2024-05-27T11:46:38.905569image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 284467
28.5%
2 132830
13.3%
7 108833
 
10.9%
L 83058
 
8.3%
3 72067
 
7.2%
1 65368
 
6.6%
0 54012
 
5.4%
8 36263
 
3.6%
5 35985
 
3.6%
4 34098
 
3.4%
Other values (8) 90031
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 602613
60.4%
Uppercase Letter 394399
39.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 132830
22.0%
7 108833
18.1%
3 72067
12.0%
1 65368
10.8%
0 54012
9.0%
8 36263
 
6.0%
5 35985
 
6.0%
4 34098
 
5.7%
9 31814
 
5.3%
6 31343
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
S 284467
72.1%
L 83058
 
21.1%
T 12167
 
3.1%
P 8173
 
2.1%
C 4255
 
1.1%
R 2014
 
0.5%
N 263
 
0.1%
O 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 602613
60.4%
Latin 394399
39.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 132830
22.0%
7 108833
18.1%
3 72067
12.0%
1 65368
10.8%
0 54012
9.0%
8 36263
 
6.0%
5 35985
 
6.0%
4 34098
 
5.7%
9 31814
 
5.3%
6 31343
 
5.2%
Latin
ValueCountFrequency (%)
S 284467
72.1%
L 83058
 
21.1%
T 12167
 
3.1%
P 8173
 
2.1%
C 4255
 
1.1%
R 2014
 
0.5%
N 263
 
0.1%
O 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 997012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 284467
28.5%
2 132830
13.3%
7 108833
 
10.9%
L 83058
 
8.3%
3 72067
 
7.2%
1 65368
 
6.6%
0 54012
 
5.4%
8 36263
 
3.6%
5 35985
 
3.6%
4 34098
 
3.4%
Other values (8) 90031
 
9.0%
Distinct495
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2024-05-27T11:46:39.257717image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length42
Median length34
Mean length19.890129
Min length7

Characters and Unicode

Total characters3912269
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowCT-BENGALURU-RESIDENCYROAD
2nd rowCT-HYDERABAD-G.S.CENTERPOINT
3rd rowCT-HYDERABAD-G.S.CENTERPOINT
4th rowCT-MUMBAI-GOREGAON-OBEROIMALL
5th rowCT-BENGALURU-J.P.NAGAR
ValueCountFrequency (%)
10445
 
3.5%
city 6054
 
2.0%
mall 4504
 
1.5%
kolkata 4487
 
1.5%
ls 3230
 
1.1%
115-ssl-saltlakekolkata 3219
 
1.1%
phoenix 3088
 
1.0%
market 3027
 
1.0%
lsdlfmall-noida 2557
 
0.9%
128-ssl-southcitykolkata 2514
 
0.9%
Other values (725) 252501
85.4%
2024-05-27T11:46:39.819909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 323272
 
8.3%
S 297250
 
7.6%
L 242906
 
6.2%
A 242844
 
6.2%
a 190404
 
4.9%
T 113059
 
2.9%
R 109736
 
2.8%
l 100342
 
2.6%
97900
 
2.5%
e 97295
 
2.5%
Other values (60) 2097261
53.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2069465
52.9%
Lowercase Letter 1098598
28.1%
Dash Punctuation 323272
 
8.3%
Decimal Number 314695
 
8.0%
Space Separator 98936
 
2.5%
Open Punctuation 3220
 
0.1%
Close Punctuation 3220
 
0.1%
Other Punctuation 863
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 297250
14.4%
L 242906
11.7%
A 242844
11.7%
T 113059
 
5.5%
R 109736
 
5.3%
N 96098
 
4.6%
O 93824
 
4.5%
M 92428
 
4.5%
I 90853
 
4.4%
C 83882
 
4.1%
Other values (16) 606585
29.3%
Lowercase Letter
ValueCountFrequency (%)
a 190404
17.3%
l 100342
 
9.1%
e 97295
 
8.9%
i 92417
 
8.4%
r 78042
 
7.1%
n 77716
 
7.1%
t 65706
 
6.0%
o 61247
 
5.6%
h 44585
 
4.1%
u 38932
 
3.5%
Other values (16) 251912
22.9%
Decimal Number
ValueCountFrequency (%)
1 93732
29.8%
2 46618
14.8%
4 30890
 
9.8%
5 29694
 
9.4%
8 23274
 
7.4%
6 19419
 
6.2%
9 18480
 
5.9%
7 18320
 
5.8%
3 18110
 
5.8%
0 16158
 
5.1%
Other Punctuation
ValueCountFrequency (%)
& 638
73.9%
. 204
 
23.6%
, 21
 
2.4%
Space Separator
ValueCountFrequency (%)
97900
99.0%
  1036
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 323272
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3220
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3168063
81.0%
Common 744206
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 297250
 
9.4%
L 242906
 
7.7%
A 242844
 
7.7%
a 190404
 
6.0%
T 113059
 
3.6%
R 109736
 
3.5%
l 100342
 
3.2%
e 97295
 
3.1%
N 96098
 
3.0%
O 93824
 
3.0%
Other values (42) 1584305
50.0%
Common
ValueCountFrequency (%)
- 323272
43.4%
97900
 
13.2%
1 93732
 
12.6%
2 46618
 
6.3%
4 30890
 
4.2%
5 29694
 
4.0%
8 23274
 
3.1%
6 19419
 
2.6%
9 18480
 
2.5%
7 18320
 
2.5%
Other values (8) 42607
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3911233
> 99.9%
None 1036
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 323272
 
8.3%
S 297250
 
7.6%
L 242906
 
6.2%
A 242844
 
6.2%
a 190404
 
4.9%
T 113059
 
2.9%
R 109736
 
2.8%
l 100342
 
2.6%
97900
 
2.5%
e 97295
 
2.5%
Other values (59) 2096225
53.6%
None
ValueCountFrequency (%)
  1036
100.0%

MRP
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1631.6023
Minimum399
Maximum4995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-05-27T11:46:40.039873image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile499
Q1595
median1595
Q32495
95-th percentile3095
Maximum4995
Range4596
Interquartile range (IQR)1900

Descriptive statistics

Standard deviation995.23332
Coefficient of variation (CV)0.609973
Kurtosis-0.56123143
Mean1631.6023
Median Absolute Deviation (MAD)1000
Skewness0.41288003
Sum3.2092637 × 108
Variance990489.36
MonotonicityNot monotonic
2024-05-27T11:46:40.287382image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
499 47180
24.0%
1995 24104
12.3%
2595 23515
12.0%
645 23482
11.9%
1595 20119
10.2%
1895 8738
 
4.4%
2695 8717
 
4.4%
2295 8446
 
4.3%
3995 6358
 
3.2%
595 4599
 
2.3%
Other values (24) 21436
10.9%
ValueCountFrequency (%)
399 181
 
0.1%
499 47180
24.0%
545 231
 
0.1%
595 4599
 
2.3%
645 23482
11.9%
845 681
 
0.3%
895 167
 
0.1%
1195 13
 
< 0.1%
1295 26
 
< 0.1%
1345 1588
 
0.8%
ValueCountFrequency (%)
4995 33
 
< 0.1%
4795 197
 
0.1%
4590 794
 
0.4%
4190 411
 
0.2%
3995 6358
 
3.2%
3095 2142
 
1.1%
2995 4287
 
2.2%
2795 1548
 
0.8%
2695 8717
 
4.4%
2595 23515
12.0%

Gross UCP
Real number (ℝ)

Distinct246
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1920.3982
Minimum-28710
Maximum142125
Zeros8
Zeros (%)< 0.1%
Negative593
Negative (%)0.3%
Memory size3.0 MiB
2024-05-27T11:46:40.523335image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-28710
5-th percentile499
Q1645
median1895
Q32595
95-th percentile4590
Maximum142125
Range170835
Interquartile range (IQR)1950

Descriptive statistics

Standard deviation1771.1415
Coefficient of variation (CV)0.92227824
Kurtosis592.61172
Mean1920.3982
Median Absolute Deviation (MAD)897
Skewness11.699347
Sum3.7773081 × 108
Variance3136942.2
MonotonicityNot monotonic
2024-05-27T11:46:40.803126image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499 40157
20.4%
645 20758
10.6%
1995 20744
10.5%
2595 20518
10.4%
1595 15517
 
7.9%
2295 7777
 
4.0%
2695 7765
 
3.9%
1895 7731
 
3.9%
3995 5789
 
2.9%
998 5583
 
2.8%
Other values (236) 44355
22.6%
ValueCountFrequency (%)
-28710 1
 
< 0.1%
-11985 1
 
< 0.1%
-7990 1
 
< 0.1%
-5990 1
 
< 0.1%
-5390 2
 
< 0.1%
-4990 1
 
< 0.1%
-4795 1
 
< 0.1%
-4590 4
 
< 0.1%
-4190 3
 
< 0.1%
-3995 27
< 0.1%
ValueCountFrequency (%)
142125 1
< 0.1%
134775 1
< 0.1%
125790 1
< 0.1%
91590 1
< 0.1%
66990 1
< 0.1%
59900 1
< 0.1%
59670 1
< 0.1%
59015 1
< 0.1%
52635 1
< 0.1%
47880 1
< 0.1%

Net UCP
Real number (ℝ)

Distinct3643
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1749.921
Minimum-17982
Maximum127912.5
Zeros885
Zeros (%)0.4%
Negative590
Negative (%)0.3%
Memory size3.0 MiB
2024-05-27T11:46:41.074548image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-17982
5-th percentile449.5
Q1645
median1695.75
Q32335.5
95-th percentile3995
Maximum127912.5
Range145894.5
Interquartile range (IQR)1690.5

Descriptive statistics

Standard deviation1488.0559
Coefficient of variation (CV)0.85035608
Kurtosis597.84683
Mean1749.921
Median Absolute Deviation (MAD)899.25
Skewness10.525224
Sum3.4419896 × 108
Variance2214310.5
MonotonicityNot monotonic
2024-05-27T11:46:41.264736image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
499 30691
 
15.6%
645 18774
 
9.5%
2595 11690
 
5.9%
1995 11003
 
5.6%
1595 9654
 
4.9%
1895 5675
 
2.9%
999 5548
 
2.8%
1695.75 5125
 
2.6%
2295 4947
 
2.5%
3995 4918
 
2.5%
Other values (3633) 88669
45.1%
ValueCountFrequency (%)
-17982 1
 
< 0.1%
-11985 1
 
< 0.1%
-7990 1
 
< 0.1%
-5091.5 1
 
< 0.1%
-4795 1
 
< 0.1%
-4590 2
 
< 0.1%
-4390 2
 
< 0.1%
-4190 3
 
< 0.1%
-3995 25
< 0.1%
-3992 1
 
< 0.1%
ValueCountFrequency (%)
127912.5 1
< 0.1%
107820 1
< 0.1%
106921.5 1
< 0.1%
78045 1
< 0.1%
50915 1
< 0.1%
47736 1
< 0.1%
43890 1
< 0.1%
43092 1
< 0.1%
41958 1
< 0.1%
36963 1
< 0.1%
Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2024-05-27T11:46:41.606082image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length20
Median length17
Mean length6.8919184
Min length3

Characters and Unicode

Total characters1355599
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBangalore
2nd rowHyderabad
3rd rowHyderabad
4th rowMumbai
5th rowBangalore
ValueCountFrequency (%)
mumbai 24938
 
12.7%
kolkata 22125
 
11.2%
bangalore 21733
 
11.0%
delhi 14455
 
7.3%
pune 12677
 
6.4%
hyderabad 9323
 
4.7%
noida 9242
 
4.7%
chennai 6603
 
3.4%
gurgaon 5701
 
2.9%
lucknow 4688
 
2.4%
Other values (87) 65361
33.2%
2024-05-27T11:46:42.184661image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 244292
18.0%
i 86769
 
6.4%
o 83624
 
6.2%
e 81597
 
6.0%
n 79925
 
5.9%
r 78039
 
5.8%
u 77605
 
5.7%
l 68626
 
5.1%
d 50838
 
3.8%
h 49576
 
3.7%
Other values (40) 454708
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1148117
84.7%
Uppercase Letter 202701
 
15.0%
Space Separator 1746
 
0.1%
Decimal Number 1260
 
0.1%
Dash Punctuation 935
 
0.1%
Open Punctuation 420
 
< 0.1%
Close Punctuation 420
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 244292
21.3%
i 86769
 
7.6%
o 83624
 
7.3%
e 81597
 
7.1%
n 79925
 
7.0%
r 78039
 
6.8%
u 77605
 
6.8%
l 68626
 
6.0%
d 50838
 
4.4%
h 49576
 
4.3%
Other values (13) 247226
21.5%
Uppercase Letter
ValueCountFrequency (%)
B 29163
14.4%
M 28526
14.1%
K 25402
12.5%
D 17011
8.4%
P 15364
7.6%
G 14217
7.0%
N 12291
 
6.1%
C 12057
 
5.9%
H 10380
 
5.1%
A 7288
 
3.6%
Other values (10) 31002
15.3%
Decimal Number
ValueCountFrequency (%)
2 420
33.3%
3 420
33.3%
0 420
33.3%
Space Separator
ValueCountFrequency (%)
1746
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 935
100.0%
Open Punctuation
ValueCountFrequency (%)
( 420
100.0%
Close Punctuation
ValueCountFrequency (%)
) 420
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1350818
99.6%
Common 4781
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 244292
18.1%
i 86769
 
6.4%
o 83624
 
6.2%
e 81597
 
6.0%
n 79925
 
5.9%
r 78039
 
5.8%
u 77605
 
5.7%
l 68626
 
5.1%
d 50838
 
3.8%
h 49576
 
3.7%
Other values (33) 449927
33.3%
Common
ValueCountFrequency (%)
1746
36.5%
- 935
19.6%
( 420
 
8.8%
2 420
 
8.8%
3 420
 
8.8%
0 420
 
8.8%
) 420
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1355599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 244292
18.0%
i 86769
 
6.4%
o 83624
 
6.2%
e 81597
 
6.0%
n 79925
 
5.9%
r 78039
 
5.8%
u 77605
 
5.7%
l 68626
 
5.1%
d 50838
 
3.8%
h 49576
 
3.7%
Other values (40) 454708
33.5%
Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
MAHARASTRA
44256 
WESTBENGAL
31145 
KARNATAKA
25078 
UTTARPRADESH
19320 
DELHI
15893 
Other values (25)
61002 

Length

Max length20
Median length17
Mean length10.877937
Min length3

Characters and Unicode

Total characters2139625
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKARNATAKA
2nd rowTELANGANA
3rd rowTELANGANA
4th rowMAHARASTRA
5th rowKARNATAKA

Common Values

ValueCountFrequency (%)
MAHARASTRA 44256
22.5%
WESTBENGAL 31145
15.8%
KARNATAKA 25078
12.7%
UTTARPRADESH 19320
9.8%
DELHI 15893
 
8.1%
TAMILNADU 9179
 
4.7%
TELANGANA 8510
 
4.3%
HARYANA 8224
 
4.2%
GUJARAT 7717
 
3.9%
MADHYA PRADESH 6033
 
3.1%
Other values (20) 21339
10.8%

Length

2024-05-27T11:46:42.412666image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maharastra 44256
21.3%
westbengal 31145
15.0%
karnataka 25078
12.1%
uttarpradesh 19320
9.3%
delhi 15893
 
7.7%
pradesh 10126
 
4.9%
telangana 9325
 
4.5%
tamilnadu 9179
 
4.4%
haryana 8224
 
4.0%
gujarat 7717
 
3.7%
Other values (18) 27077
13.1%

Most occurring characters

ValueCountFrequency (%)
A 492754
23.0%
317734
14.8%
R 194225
 
9.1%
T 172674
 
8.1%
H 120084
 
5.6%
E 118252
 
5.5%
S 113252
 
5.3%
N 103003
 
4.8%
L 66988
 
3.1%
D 66941
 
3.1%
Other values (26) 373718
17.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1812537
84.7%
Space Separator 317734
 
14.8%
Lowercase Letter 9094
 
0.4%
Other Punctuation 260
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 492754
27.2%
R 194225
 
10.7%
T 172674
 
9.5%
H 120084
 
6.6%
E 118252
 
6.5%
S 113252
 
6.2%
N 103003
 
5.7%
L 66988
 
3.7%
D 66941
 
3.7%
M 61013
 
3.4%
Other values (11) 303351
16.7%
Lowercase Letter
ValueCountFrequency (%)
a 3302
36.3%
n 1708
18.8%
l 1134
 
12.5%
e 1129
 
12.4%
g 850
 
9.3%
r 357
 
3.9%
h 176
 
1.9%
o 111
 
1.2%
d 78
 
0.9%
u 73
 
0.8%
Other values (3) 176
 
1.9%
Space Separator
ValueCountFrequency (%)
317734
100.0%
Other Punctuation
ValueCountFrequency (%)
& 260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1821631
85.1%
Common 317994
 
14.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 492754
27.1%
R 194225
 
10.7%
T 172674
 
9.5%
H 120084
 
6.6%
E 118252
 
6.5%
S 113252
 
6.2%
N 103003
 
5.7%
L 66988
 
3.7%
D 66941
 
3.7%
M 61013
 
3.3%
Other values (24) 312445
17.2%
Common
ValueCountFrequency (%)
317734
99.9%
& 260
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2139625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 492754
23.0%
317734
14.8%
R 194225
 
9.1%
T 172674
 
8.1%
H 120084
 
5.6%
E 118252
 
5.5%
S 113252
 
5.3%
N 103003
 
4.8%
L 66988
 
3.1%
D 66941
 
3.1%
Other values (26) 373718
17.5%

Billing grp
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
LFS
196694 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters590082
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLFS
2nd rowLFS
3rd rowLFS
4th rowLFS
5th rowLFS

Common Values

ValueCountFrequency (%)
LFS 196694
100.0%

Length

2024-05-27T11:46:42.569187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T11:46:42.695738image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
lfs 196694
100.0%

Most occurring characters

ValueCountFrequency (%)
L 196694
33.3%
F 196694
33.3%
S 196694
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 590082
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 196694
33.3%
F 196694
33.3%
S 196694
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 590082
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 196694
33.3%
F 196694
33.3%
S 196694
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 590082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 196694
33.3%
F 196694
33.3%
S 196694
33.3%

Channel-1
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
LFS
196694 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters590082
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLFS
2nd rowLFS
3rd rowLFS
4th rowLFS
5th rowLFS

Common Values

ValueCountFrequency (%)
LFS 196694
100.0%

Length

2024-05-27T11:46:42.822225image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T11:46:42.965456image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
lfs 196694
100.0%

Most occurring characters

ValueCountFrequency (%)
L 196694
33.3%
F 196694
33.3%
S 196694
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 590082
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 196694
33.3%
F 196694
33.3%
S 196694
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 590082
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 196694
33.3%
F 196694
33.3%
S 196694
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 590082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 196694
33.3%
F 196694
33.3%
S 196694
33.3%
Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2024-05-27T11:46:43.212637image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length40
Median length25
Mean length17.565513
Min length11

Characters and Unicode

Total characters3455031
Distinct characters61
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row3. 150 M Amalfi Men deo
2nd row3. 20 M Verge
3rd row8. Deo M C. Road 150 ml
4th row3. Tales W Ibiza 100 ml
5th row7. 100 W Pristine
ValueCountFrequency (%)
m 111850
 
11.4%
w 82781
 
8.5%
deo 63370
 
6.5%
ml 61898
 
6.3%
100 58007
 
5.9%
150 47361
 
4.8%
1 44321
 
4.5%
raw 32964
 
3.4%
2 29844
 
3.1%
3 29693
 
3.0%
Other values (69) 415995
42.5%
2024-05-27T11:46:43.738436image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
781390
22.6%
e 296242
 
8.6%
0 229512
 
6.6%
. 218260
 
6.3%
1 151628
 
4.4%
M 130243
 
3.8%
l 126499
 
3.7%
o 123840
 
3.6%
5 98076
 
2.8%
a 89543
 
2.6%
Other values (51) 1209798
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1180892
34.2%
Space Separator 781390
22.6%
Decimal Number 665363
19.3%
Uppercase Letter 547937
15.9%
Other Punctuation 242207
 
7.0%
Open Punctuation 17241
 
0.5%
Close Punctuation 17241
 
0.5%
Dash Punctuation 1435
 
< 0.1%
Math Symbol 1325
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 296242
25.1%
l 126499
10.7%
o 123840
10.5%
a 89543
 
7.6%
m 78163
 
6.6%
t 64626
 
5.5%
r 59007
 
5.0%
i 57804
 
4.9%
s 51354
 
4.3%
f 43405
 
3.7%
Other values (14) 190409
16.1%
Uppercase Letter
ValueCountFrequency (%)
M 130243
23.8%
W 86225
15.7%
C 66603
12.2%
R 60325
11.0%
D 58884
10.7%
S 29264
 
5.3%
A 28621
 
5.2%
N 23713
 
4.3%
H 15066
 
2.7%
V 14694
 
2.7%
Other values (9) 34299
 
6.3%
Decimal Number
ValueCountFrequency (%)
0 229512
34.5%
1 151628
22.8%
5 98076
14.7%
2 69262
 
10.4%
3 41831
 
6.3%
4 28717
 
4.3%
6 19501
 
2.9%
9 10091
 
1.5%
7 9433
 
1.4%
8 7312
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 218260
90.1%
, 15806
 
6.5%
& 8141
 
3.4%
Space Separator
ValueCountFrequency (%)
781390
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17241
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17241
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1435
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1325
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1728829
50.0%
Common 1726202
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 296242
17.1%
M 130243
 
7.5%
l 126499
 
7.3%
o 123840
 
7.2%
a 89543
 
5.2%
W 86225
 
5.0%
m 78163
 
4.5%
C 66603
 
3.9%
t 64626
 
3.7%
R 60325
 
3.5%
Other values (33) 606520
35.1%
Common
ValueCountFrequency (%)
781390
45.3%
0 229512
 
13.3%
. 218260
 
12.6%
1 151628
 
8.8%
5 98076
 
5.7%
2 69262
 
4.0%
3 41831
 
2.4%
4 28717
 
1.7%
6 19501
 
1.1%
( 17241
 
1.0%
Other values (8) 70784
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3455031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
781390
22.6%
e 296242
 
8.6%
0 229512
 
6.6%
. 218260
 
6.3%
1 151628
 
4.4%
M 130243
 
3.8%
l 126499
 
3.7%
o 123840
 
3.6%
5 98076
 
2.8%
a 89543
 
2.6%
Other values (51) 1209798
35.0%

Collection
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
d. Gift Pack
33257 
g. Premium Deo
33186 
a. Classic 100 ml
32271 
f. Classic 20 ml
28312 
e. Aqua
17234 
Other values (9)
52434 

Length

Max length17
Median length15
Mean length13.892183
Min length7

Characters and Unicode

Total characters2732509
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowg. Premium Deo
2nd rowf. Classic 20 ml
3rd rowg. Escapade Deo
4th rowI. Tales 100ml
5th rowa. Classic 100 ml

Common Values

ValueCountFrequency (%)
d. Gift Pack 33257
16.9%
g. Premium Deo 33186
16.9%
a. Classic 100 ml 32271
16.4%
f. Classic 20 ml 28312
14.4%
e. Aqua 17234
8.8%
g. Escapade Deo 14175
7.2%
b. Classic 50 ml 12523
 
6.4%
c. Escapade 10201
 
5.2%
j1. Nox 100 ml 6358
 
3.2%
I. Tales 100ml 6183
 
3.1%
Other values (4) 2994
 
1.5%

Length

2024-05-27T11:46:44.156571image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ml 79464
12.4%
classic 73106
 
11.4%
g 47361
 
7.4%
deo 47361
 
7.4%
100 38629
 
6.0%
d 33257
 
5.2%
pack 33257
 
5.2%
gift 33257
 
5.2%
premium 33186
 
5.2%
a 32271
 
5.0%
Other values (21) 192252
29.9%

Most occurring characters

ValueCountFrequency (%)
446707
16.3%
a 214645
 
7.9%
. 196694
 
7.2%
s 177619
 
6.5%
l 167082
 
6.1%
m 154165
 
5.6%
c 141788
 
5.2%
i 139553
 
5.1%
0 134743
 
4.9%
e 128344
 
4.7%
Other values (31) 831169
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1572431
57.5%
Space Separator 446707
 
16.3%
Uppercase Letter 285641
 
10.5%
Decimal Number 231032
 
8.5%
Other Punctuation 196694
 
7.2%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 214645
13.7%
s 177619
11.3%
l 167082
10.6%
m 154165
9.8%
c 141788
9.0%
i 139553
8.9%
e 128344
8.2%
f 61569
 
3.9%
d 57637
 
3.7%
o 55869
 
3.6%
Other values (12) 274160
17.4%
Uppercase Letter
ValueCountFrequency (%)
C 73106
25.6%
P 66443
23.3%
D 47361
16.6%
G 33257
11.6%
E 24376
 
8.5%
A 17234
 
6.0%
N 8500
 
3.0%
I 6183
 
2.2%
T 6183
 
2.2%
W 2142
 
0.7%
Other values (2) 856
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 134743
58.3%
1 53312
 
23.1%
2 30454
 
13.2%
5 12523
 
5.4%
Space Separator
ValueCountFrequency (%)
446707
100.0%
Other Punctuation
ValueCountFrequency (%)
. 196694
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1858072
68.0%
Common 874437
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 214645
11.6%
s 177619
 
9.6%
l 167082
 
9.0%
m 154165
 
8.3%
c 141788
 
7.6%
i 139553
 
7.5%
e 128344
 
6.9%
C 73106
 
3.9%
P 66443
 
3.6%
f 61569
 
3.3%
Other values (24) 533758
28.7%
Common
ValueCountFrequency (%)
446707
51.1%
. 196694
22.5%
0 134743
 
15.4%
1 53312
 
6.1%
2 30454
 
3.5%
5 12523
 
1.4%
- 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2732509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
446707
16.3%
a 214645
 
7.9%
. 196694
 
7.2%
s 177619
 
6.5%
l 167082
 
6.1%
m 154165
 
5.6%
c 141788
 
5.2%
i 139553
 
5.1%
0 134743
 
4.9%
e 128344
 
4.7%
Other values (31) 831169
30.4%

Interactions

2024-05-27T11:46:29.938419image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:27.999553image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:28.686896image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:29.383114image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:30.145627image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:28.204692image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:28.858618image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:29.525211image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:30.317557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:28.332093image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:29.037550image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:29.655653image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:30.459602image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:28.493058image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:29.208931image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-05-27T11:46:29.780519image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Missing values

2024-05-27T11:46:30.781389image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-27T11:46:31.460900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeBilling grpChannel-1Variant NameCollection
02022-232022-04-01AprQ1South2022-04-01NFM03DQ14GTFCNCN1200CN1200CT-BENGALURU-RESIDENCYROAD49919961996.0BangaloreKARNATAKALFSLFS3. 150 M Amalfi Men deog. Premium Deo
12022-232022-04-01AprQ1South2022-04-01NDFM13PD11GTFCNCN1201CN1201CT-HYDERABAD-G.S.CENTERPOINT595595595.0HyderabadTELANGANALFSLFS3. 20 M Vergef. Classic 20 ml
22022-232022-04-01AprQ1South2022-04-01NFM05DQ11GTFCNCN1201CN1201CT-HYDERABAD-G.S.CENTERPOINT499499499.0HyderabadTELANGANALFSLFS8. Deo M C. Road 150 mlg. Escapade Deo
32022-232022-04-01AprQ1West2022-04-01FW19PC11LTFCNCN1206CN1206CT-MUMBAI-GOREGAON-OBEROIMALL159515951595.0MumbaiMAHARASTRALFSLFS3. Tales W Ibiza 100 mlI. Tales 100ml
42022-232022-04-01AprQ1South2022-04-01NEFW05PGC1LTFCNCN1212CN1212CT-BENGALURU-J.P.NAGAR229522952295.0BangaloreKARNATAKALFSLFS7. 100 W Pristinea. Classic 100 ml
52022-232022-04-01AprQ1South2022-04-01NFM01DQ11GTFCNCN1212CN1212CT-BENGALURU-J.P.NAGAR499499499.0BangaloreKARNATAKALFSLFS1. 150 M Raw Deog. Premium Deo
62022-232022-04-01AprQ1South2022-04-01NFFM05PG21GTFCNCN1212CN1212CT-BENGALURU-J.P.NAGAR199519951995.0BangaloreKARNATAKALFSLFS2. Mini 25 ml M (R,V)d. Gift Pack
72022-232022-04-01AprQ1West2022-04-01NFM02DQ11GTFCNCN1231CN1231CT-PIMPRI-CITYCENTREMALL499499499.0PuneMAHARASTRALFSLFS2. 150 M Steele Deog. Premium Deo
82022-232022-04-01AprQ1West2022-04-01NFW02DQ11LTFCNCN1231CN1231CT-PIMPRI-CITYCENTREMALL499499499.0PuneMAHARASTRALFSLFS5. 150 W Nude Deog. Premium Deo
92022-232022-04-01AprQ1North2022-04-01NEFM01PGC1GTFCNCN1239CN1239CT-NEWDELHI-WORLDMARK229522951951.0DelhiDELHILFSLFS1. 100 M Rawa. Classic 100 ml
YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeBilling grpChannel-1Variant NameCollection
8469252022-232023-03-01MarQ4West2023-03-31NEFW12PD11LTFCTCT0031CT0031CT-NASHIK-CITY CENTER-LAWATE N645645645.0NashikMAHARASTRALFSLFS5. 20 W Nudef. Classic 20 ml
8469262022-232023-03-01MarQ4South2023-03-31NGFM08PC11GTFCTCT0019CT0019CT-THIRUVANANTHAPURAM-ARTECH W-TRIVENDRUM269526952695.0ThiruvananthapuramKeralaLFSLFS2. 100 M C.Roadc. Escapade
8469272022-232023-03-01MarQ4South2023-03-31NFM01DQ11GTFCTCT0014CT0014CT-BENGALURU-SOUL SPACE SPIRIT499499499.0BangaloreKARNATAKALFSLFS1. 150 M Raw Deog. Premium Deo
8469282022-232023-03-01MarQ4South2023-03-31NEFW13PD11LTFCTCT0014CT0014CT-BENGALURU-SOUL SPACE SPIRIT645645645.0BangaloreKARNATAKALFSLFS6. 20 W Sheerf. Classic 20 ml
8469292022-232023-03-01MarQ4South2023-03-31NFFM14PK11GTFCTCT0014CT0014CT-BENGALURU-SOUL SPACE SPIRIT279527952795.0BangaloreKARNATAKALFSLFS2. Aqua M 90 mle. Aqua
8469302022-232023-03-01MarQ4South2023-03-31NFFW01CL21LTFCTCT0014CT0014CT-BENGALURU-SOUL SPACE SPIRIT199519951995.0BangaloreKARNATAKALFSLFS6. Coffret Deo W Celested. Gift Pack
8469312022-232023-03-01MarQ4South2023-03-31NFFP01PG21UTFCTCT0014CT0014CT-BENGALURU-SOUL SPACE SPIRIT199519951995.0BangaloreKARNATAKALFSLFS4. His & Her Mini 25 ml (V,S)d. Gift Pack
8469322022-232023-03-01MarQ4South2023-03-31NFM05DQ11GTFCTCT0014CT0014CT-BENGALURU-SOUL SPACE SPIRIT499499499.0BangaloreKARNATAKALFSLFS8. Deo M C. Road 150 mlg. Escapade Deo
8469332022-232023-03-01MarQ4South2023-03-31FW24PC11LTFCTCT0014CT0014CT-BENGALURU-SOUL SPACE SPIRIT399539953495.0BangaloreKARNATAKALFSLFS2. Nox W 100 mlj1. Nox 100 ml
8469342022-232023-03-01MarQ4North2023-03-31NFFM04PGC1GTFCTCT0006CT0006CT-NOIDA-GREAT INDIA PLACE259525952595.0NoidaUTTARPRADESHLFSLFS3. 100 M Vergea. Classic 100 ml

Duplicate rows

Most frequently occurring

YearMonthMonth KeyQTRRegionInvoice DateMaterialQuantityGenderBrandChannelFranchisee storeBill to Party codeBill to Party NameMRPGross UCPNet UCPBill to Party CityShiptopartyStateCodeBilling grpChannel-1Variant NameCollection# duplicates
02022-232022-04-01AprQ1East2022-04-10NEFM02PFC1GTFSSSS221SS221112-SSL-ELGINROADKOLKATA229522952295.00KolkataWESTBENGALLFSLFS2. 100 M Steelea. Classic 100 ml2
12022-232022-04-01AprQ1East2022-04-15NEFW14PD11LTFSSSS283SS283188-SSL-ACROPOLISKOLKATA645645503.49KolkataWESTBENGALLFSLFS7. 20 W Pristinef. Classic 20 ml2
22022-232022-04-01AprQ1East2022-04-28NEFW11PD11LTFSSSS283SS283188-SSL-ACROPOLISKOLKATA645645645.00KolkataWESTBENGALLFSLFS4. 20 W Celestef. Classic 20 ml2
32022-232022-04-01AprQ1North2022-04-01NEFW02PFC1LTFSSSS216SS216121-SSL-LUCKNOW229522952295.00LucknowUTTARPRADESHLFSLFS4. 100 W Celestea. Classic 100 ml2
42022-232022-04-01AprQ1North2022-04-02NFM01DQ11GTFSSSS309SS309478-SHOPPERSSTOP-TAPASYAONE499499499.00GurgaonHARYANALFSLFS1. 150 M Raw Deog. Premium Deo2
52022-232022-04-01AprQ1North2022-04-03NEFP01PGFL1PTFSSSS216SS216121-SSL-LUCKNOW269526952695.00LucknowUTTARPRADESHLFSLFS1. His & Her 50 ml (R,C)d. Gift Pack2
62022-232022-04-01AprQ1North2022-04-04FM01HQ21GTFSSSS282SS282267-SSL-JANAKPURI184518451845.00DelhiDELHILFSLFS10. Amalfi Coffret M EDP + Deod. Gift Pack2
72022-232022-04-01AprQ1South2022-04-05NEFP01PGFL1PTFSSSS227SS227154-SSL-CYBERABADINORBIT269526952695.00HyderabadTELANGANALFSLFS1. His & Her 50 ml (R,C)d. Gift Pack2
82022-232022-04-01AprQ1South2022-04-28NEFM01PGL1GTFSSSS242SS242161-SSL-MYSORE159515951595.00MysoreKARNATAKALFSLFS1. 50 M Rawb. Classic 50 ml2
92022-232022-05-01MayQ1East2022-05-02FM21PC11GTFSSSS303SS303314-SSL-GUWAHATICITYCENTER399539953995.00GuwahatiWESTBENGALLFSLFS1. Nox M 100 mlj1. Nox 100 ml2